The problem of extracting a usable signal from a noisy environment is a common one in the field of signal processing. Noise is generally defined as an unwanted or interfering component that degrades system performance and detracts from the processing of the desired signal. Noise may be provided by numerous sources both external and internal to a signal processing device or receiver. In some applications, an incoming signal may be corrupted by strong additive noise such as, for example, weak radar echoes from a target in the presence of strong background clutter. In some cases, signals traveling over existing communications systems experience phase and amplitude distortions from channel noise that is internal to the receiving device, much like tinnitus in the human ear or thermal noise in an optical or radio frequency (RF) detector. In still other applications, noise may be introduced into a received signal by deliberate and intentional interference, such as in jamming. Jamming is any intentional or deliberate signal interference utilized by hostile forces to disrupt normal receiver operation.
Because of the noise presence, receiving systems generally exhibit a sensitivity threshold that limits the processing of signals to those which exceed the noise level. The utilization of information contained in signals that are obscured or contaminated by noise is limited by the ability of the receiver processing device to separate the signal components from the noise, eliminate the noise component, or enhance the signal to a level in excess of the noise to permit the extraction of the desired information.
Radar performance, in particular, may be severely degraded in the presence of noise, “clutter,” jamming or chaff. Clutter is a term used to describe the return emanating from objects which tend to obscure the signal associated with the “true” target return. Clutter can be classified in two main categories: surface clutter and airborne or volume clutter. Surface clutter includes the radar return from trees, vegetation, ground terrain, man-made structures and the sea surface. Volume clutter may be produced by large ensembles of airborne scatterers such as that associated with chaff, rain, birds, and insects. Chaff consists of a large number of small dipole reflectors that have large radar cross section (RCS) values. It may be released by hostile aircrafts or missiles as a means of an electronic countermeasure (ECM) in an attempt to confuse a radar. Clutter is particularly troublesome for most radar detection systems because it introduces a noise-like return with a random phase and amplitude that is may exceed that of the received signal. Unlike clutter, so-called “white noise” generally introduces the same amount of noise power density across the entire operating bandwidth of the radar.
There are several known techniques for extracting signal information from noisy environments caused by one or more of the situations described above. Known techniques include, for example, matched filtering, Kalman/Weiner filters, moving target indicator (MTI) systems, and coherent integration. Each of these techniques, while suitable for some applications, suffers from limitations, as described in more detail below. All of these techniques are described in more detail in Bassem R. Mahafza, “Introduction to Radar Analysis” (CRC Press, 1998) and in Bassem R. Mahafza, “Radar Systems Analysis and Design Using MATLAB” (CRC Press, 2000).
In a system which utilizes a matched filter, the signal-to-noise ratio (SNR) of the receiver output is enhanced by or through correlation of the received signal with the transmitted wave form. In a radar system, for example, a received signal consists of an amplitude weighted and time delayed version of the transmitted signal plus noise. The receiver processes a replica of the transmitted signal together with the received signal to determine the correlation between the two signals. Maximum correlation indicates the location of the desired signal, i.e. the “true” target return, within the receiver time window. While matched filtering may work in many situations, it may be inadequate in clutter, chaff, and jamming environments. In these situations, the amount of interference signal is so overwhelming that processing requires prohibitive pulse lengths or system band widths to recover the true signal from the background interference.
Kalman filters use Markov processes to estimate the desired signals and, as such, are difficult to implement because of the mathematics involved. Furthermore, the proper selection of gain parameters requires accurate a priori knowledge of the target dynamics and is not straight-forward. Weiner filters, a subclass of Kalman filters, are effective with thermal background noise but are not generally effective against clutter or jammer returns.
In coherent integration systems, otherwise known as pulse integration systems, coherent radar signals are added together to increase the SNR. Two or more radar signals are said to be coherent if the amplitudes and relative phases of the signals have a known relationship even though they may be separated in time. If the noise is uncorrelated, the SNR increases linearly with the number of signals integrated by the processor. The accuracy of these systems are limited by their ability to integrate a sufficient number of samples over an extended period of time since fluctuations in the amplitude and phase of the target return may cause the desired signal to decorrelate and become noncoherent.
In moving target indicator (MTI) systems, delay-line cancelers are used to suppress target-like return signals produced by clutter, and allow return signals from desired targets to pass through with little degradation. Clutter is distinguishable from receiver noise by its relatively low-frequency, narrow spectrum. An MTI system cancels the clutter return from the incoming signal by subtracting successive echoes from the same location. The problem with MTI systems is that they exhibit “blind speeds,” corresponding to Doppler frequencies for which the radar is unable to detect targets.
FIG. 1 depicts the output gain of a three-pulse MTI canceller along the vertical axis with frequency shown along the horizontal axis. As shown in FIG. 1, blind speeds occur when the target velocity results in a Doppler frequency equal to an integer multiple of the radar pulse repetition frequency which, in this example, is at 100 Hz, 200 Hz, 300 Hz, etc. A target, having a relative velocity corresponding to a Doppler frequency equal to an integer multiple of the pulse repetition frequency, will not be detected. Another limitation of MTI systems is that the pass band response (gain) may vary widely over the bandwidth of interest which masks the effects of a change in the target velocity.
The performance of MTI systems can be improved by increasing the number of delay lines. FIG. 2 shows an example of a MTI system with multiple delay-line cancelers and a weighted summer. As shown in FIG. 2, the signal is tapped at multiple locations at even intervals delay time, T, seconds apart. The delayed pulses are weighted by binomial or other coefficients and then added together to produce the output of the MTI filter.
The performance of this type of MTI system is known to be dependent on the choice of weighting coefficients. There are numerous methods for choosing the weights, some of which are discussed in Fred E. Nathanson, “Radar Design Principles,” (McGraw-Hill Inc., 1991, 2nd ed.), pp. 410-12. One common weighting scheme is to choose weights that are binomial coefficients (coefficients of the expansion (1+a)n) with alternating signs. Notwithstanding the effectiveness of MTI filters with binomial coefficients, these systems still exhibit blind speeds and wide pass-band (gain) fluctuations may mask the effects of changes in the target velocity. Therefore, research continues to define alternate weighting schemes and associated coefficients to minimize the effect of blind speeds and provide a better pass-band response.